Overview

Dataset statistics

Number of variables13
Number of observations695
Missing cells117
Missing cells (%)1.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory70.7 KiB
Average record size in memory104.2 B

Variable types

Numeric12
DateTime1

Alerts

unpaid has constant value "2363179471491548.0" Constant
df_index is highly correlated with relative_hourHigh correlation
gpu_memory_usage is highly correlated with gpu_load and 1 other fieldsHigh correlation
gpu_load is highly correlated with gpu_memory_usage and 1 other fieldsHigh correlation
reported_hashrate is highly correlated with gpu_memory_usage and 1 other fieldsHigh correlation
relative_hour is highly correlated with df_indexHigh correlation
unpaid has 117 (16.8%) missing values Missing
gpu_memory_usage is highly skewed (γ1 = -26.36285265) Skewed
gpu_load is highly skewed (γ1 = -26.36285265) Skewed
reported_hashrate is highly skewed (γ1 = -26.36285265) Skewed
df_index is uniformly distributed Uniform
relative_hour is uniformly distributed Uniform
df_index has unique values Unique
ts has unique values Unique
relative_hour has unique values Unique
hashrate has 117 (16.8%) zeros Zeros

Reproduction

Analysis started2021-12-01 03:33:47.981638
Analysis finished2021-12-01 03:34:05.883594
Duration17.9 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct695
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean524
Minimum177
Maximum871
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2021-11-30T22:34:05.945694image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum177
5-th percentile211.7
Q1350.5
median524
Q3697.5
95-th percentile836.3
Maximum871
Range694
Interquartile range (IQR)347

Descriptive statistics

Standard deviation200.7735042
Coefficient of variation (CV)0.3831555424
Kurtosis-1.2
Mean524
Median Absolute Deviation (MAD)174
Skewness0
Sum364180
Variance40310
MonotonicityStrictly increasing
2021-11-30T22:34:06.077243image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1771
 
0.1%
6441
 
0.1%
6361
 
0.1%
6371
 
0.1%
6381
 
0.1%
6391
 
0.1%
6401
 
0.1%
6411
 
0.1%
6421
 
0.1%
6431
 
0.1%
Other values (685)685
98.6%
ValueCountFrequency (%)
1771
0.1%
1781
0.1%
1791
0.1%
1801
0.1%
1811
0.1%
ValueCountFrequency (%)
8711
0.1%
8701
0.1%
8691
0.1%
8681
0.1%
8671
0.1%

ts
Date

UNIQUE

Distinct695
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
Minimum2021-11-05 14:10:54-05:00
Maximum2021-11-05 16:10:57-05:00
2021-11-30T22:34:06.218107image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:34:06.424002image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

cpu_load
Real number (ℝ≥0)

Distinct14
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2717985612
Minimum0.1
Maximum3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2021-11-30T22:34:06.546874image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.1
Q10.2
median0.2
Q30.3
95-th percentile0.5
Maximum3
Range2.9
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.1834163929
Coefficient of variation (CV)0.6748247384
Kurtosis84.48812482
Mean0.2717985612
Median Absolute Deviation (MAD)0.1
Skewness7.33519095
Sum188.9
Variance0.0336415732
MonotonicityNot monotonic
2021-11-30T22:34:06.635430image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0.2345
49.6%
0.3247
35.5%
0.139
 
5.6%
0.422
 
3.2%
0.511
 
1.6%
0.610
 
1.4%
0.79
 
1.3%
0.84
 
0.6%
1.22
 
0.3%
1.52
 
0.3%
Other values (4)4
 
0.6%
ValueCountFrequency (%)
0.139
 
5.6%
0.2345
49.6%
0.3247
35.5%
0.422
 
3.2%
0.511
 
1.6%
ValueCountFrequency (%)
31
0.1%
1.81
0.1%
1.52
0.3%
1.22
0.3%
11
0.1%

cpu_freq
Real number (ℝ≥0)

Distinct682
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean951.5613813
Minimum807.25
Maximum3593.21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2021-11-30T22:34:06.751897image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum807.25
5-th percentile820.001
Q1844.04
median881.66
Q3936.2
95-th percentile1222.829
Maximum3593.21
Range2785.96
Interquartile range (IQR)92.16

Descriptive statistics

Standard deviation276.5908623
Coefficient of variation (CV)0.2906705418
Kurtosis36.91606068
Mean951.5613813
Median Absolute Deviation (MAD)41.37
Skewness5.475422712
Sum661335.16
Variance76502.50509
MonotonicityNot monotonic
2021-11-30T22:34:06.873919image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
831.693
 
0.4%
887.663
 
0.4%
854.952
 
0.3%
848.522
 
0.3%
1097.962
 
0.3%
834.522
 
0.3%
857.282
 
0.3%
1197.652
 
0.3%
10982
 
0.3%
853.712
 
0.3%
Other values (672)673
96.8%
ValueCountFrequency (%)
807.251
0.1%
807.841
0.1%
808.811
0.1%
809.721
0.1%
813.091
0.1%
ValueCountFrequency (%)
3593.211
0.1%
3593.121
0.1%
3006.561
0.1%
2623.181
0.1%
2459.391
0.1%

memory_usage
Real number (ℝ≥0)

Distinct600
Distinct (%)86.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1381687414
Minimum1238589440
Maximum1425440768
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2021-11-30T22:34:07.005403image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1238589440
5-th percentile1378270413
Q11379893248
median1381478400
Q31383993344
95-th percentile1385644851
Maximum1425440768
Range186851328
Interquartile range (IQR)4100096

Descriptive statistics

Standard deviation6362833.594
Coefficient of variation (CV)0.00460511801
Kurtosis375.0299813
Mean1381687414
Median Absolute Deviation (MAD)1880064
Skewness-15.51757716
Sum9.602727526 × 1011
Variance4.048565134 × 1013
MonotonicityNot monotonic
2021-11-30T22:34:07.151356image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13801103364
 
0.6%
13829980163
 
0.4%
13843292163
 
0.4%
13815152643
 
0.4%
13815398403
 
0.4%
13800120323
 
0.4%
13799096323
 
0.4%
13852098563
 
0.4%
13797949443
 
0.4%
13787422722
 
0.3%
Other values (590)665
95.7%
ValueCountFrequency (%)
12385894401
0.1%
13768130561
0.1%
13770588161
0.1%
13770874881
0.1%
13771571201
0.1%
ValueCountFrequency (%)
14254407681
0.1%
14243594241
0.1%
13870039041
0.1%
13867294721
0.1%
13867212801
0.1%

cpu_temp
Real number (ℝ≥0)

Distinct7
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.15971223
Minimum29
Maximum35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2021-11-30T22:34:07.264933image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum29
5-th percentile31
Q133
median33
Q334
95-th percentile34
Maximum35
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9907874946
Coefficient of variation (CV)0.02987925491
Kurtosis3.231170296
Mean33.15971223
Median Absolute Deviation (MAD)1
Skewness-1.269221602
Sum23046
Variance0.9816598594
MonotonicityNot monotonic
2021-11-30T22:34:07.345022image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
33321
46.2%
34234
33.7%
3278
 
11.2%
3526
 
3.7%
3118
 
2.6%
3011
 
1.6%
297
 
1.0%
ValueCountFrequency (%)
297
 
1.0%
3011
 
1.6%
3118
 
2.6%
3278
 
11.2%
33321
46.2%
ValueCountFrequency (%)
3526
 
3.7%
34234
33.7%
33321
46.2%
3278
 
11.2%
3118
 
2.6%

gpu_memory_usage
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3048026365
Minimum9437184
Maximum3052404736
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2021-11-30T22:34:07.430818image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum9437184
5-th percentile3052404736
Q13052404736
median3052404736
Q33052404736
95-th percentile3052404736
Maximum3052404736
Range3042967552
Interquartile range (IQR)0

Descriptive statistics

Standard deviation115426338.4
Coefficient of variation (CV)0.03786920603
Kurtosis695
Mean3048026365
Median Absolute Deviation (MAD)0
Skewness-26.36285265
Sum2.118378324 × 1012
Variance1.33232396 × 1016
MonotonicityDecreasing
2021-11-30T22:34:07.511253image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
3052404736694
99.9%
94371841
 
0.1%
ValueCountFrequency (%)
94371841
 
0.1%
3052404736694
99.9%
ValueCountFrequency (%)
3052404736694
99.9%
94371841
 
0.1%

gpu_load
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.98561151
Minimum90
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2021-11-30T22:34:07.601944image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum90
5-th percentile100
Q1100
median100
Q3100
95-th percentile100
Maximum100
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3793216209
Coefficient of variation (CV)0.003793762074
Kurtosis695
Mean99.98561151
Median Absolute Deviation (MAD)0
Skewness-26.36285265
Sum69490
Variance0.1438848921
MonotonicityDecreasing
2021-11-30T22:34:07.685385image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
100694
99.9%
901
 
0.1%
ValueCountFrequency (%)
901
 
0.1%
100694
99.9%
ValueCountFrequency (%)
100694
99.9%
901
 
0.1%

gpu_temp
Real number (ℝ≥0)

Distinct25
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.89640288
Minimum36
Maximum66
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2021-11-30T22:34:07.780330image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum36
5-th percentile64
Q165
median65
Q366
95-th percentile66
Maximum66
Range30
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.788547236
Coefficient of variation (CV)0.04296921112
Kurtosis49.64907708
Mean64.89640288
Median Absolute Deviation (MAD)0
Skewness-6.643830097
Sum45103
Variance7.775995688
MonotonicityNot monotonic
2021-11-30T22:34:07.878366image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
65399
57.4%
66256
36.8%
6410
 
1.4%
634
 
0.6%
613
 
0.4%
622
 
0.3%
602
 
0.3%
592
 
0.3%
551
 
0.1%
581
 
0.1%
Other values (15)15
 
2.2%
ValueCountFrequency (%)
361
0.1%
401
0.1%
421
0.1%
431
0.1%
451
0.1%
ValueCountFrequency (%)
66256
36.8%
65399
57.4%
6410
 
1.4%
634
 
0.6%
622
 
0.3%

hashrate
Real number (ℝ≥0)

ZEROS

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2129496.401
Minimum0
Maximum3333333.33
Zeros117
Zeros (%)16.8%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2021-11-30T22:34:07.971855image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12222222.22
median2222222.22
Q33333333.33
95-th percentile3333333.33
Maximum3333333.33
Range3333333.33
Interquartile range (IQR)1111111.11

Descriptive statistics

Standard deviation1066307.469
Coefficient of variation (CV)0.500732224
Kurtosis0.1075438855
Mean2129496.401
Median Absolute Deviation (MAD)0
Skewness-0.9803059005
Sum1479999999
Variance1.137011618 × 1012
MonotonicityNot monotonic
2021-11-30T22:34:08.049590image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=3)
ValueCountFrequency (%)
2222222.22402
57.8%
3333333.33176
25.3%
0117
 
16.8%
ValueCountFrequency (%)
0117
 
16.8%
2222222.22402
57.8%
3333333.33176
25.3%
ValueCountFrequency (%)
3333333.33176
25.3%
2222222.22402
57.8%
0117
 
16.8%

unpaid
Real number (ℝ≥0)

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)0.2%
Missing117
Missing (%)16.8%
Infinite0
Infinite (%)0.0%
Mean2.363179471 × 1015
Minimum2.363179471 × 1015
Maximum2.363179471 × 1015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2021-11-30T22:34:08.131905image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum2.363179471 × 1015
5-th percentile2.363179471 × 1015
Q12.363179471 × 1015
median2.363179471 × 1015
Q32.363179471 × 1015
95-th percentile2.363179471 × 1015
Maximum2.363179471 × 1015
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.500433088
Coefficient of variation (CV)2.117626249 × 10-16
Kurtosis-2.006956522
Mean2.363179471 × 1015
Median Absolute Deviation (MAD)0
Skewness1.002603791
Sum1.365917735 × 1018
Variance0.2504332756
MonotonicityIncreasing
2021-11-30T22:34:08.286283image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
2.363179471 × 1015578
83.2%
(Missing)117
 
16.8%
ValueCountFrequency (%)
2.363179471 × 1015578
83.2%
ValueCountFrequency (%)
2.363179471 × 1015578
83.2%

reported_hashrate
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1369802.878
Minimum1233000
Maximum1370000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2021-11-30T22:34:08.364520image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1233000
5-th percentile1370000
Q11370000
median1370000
Q31370000
95-th percentile1370000
Maximum1370000
Range137000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5196.706206
Coefficient of variation (CV)0.003793762074
Kurtosis695
Mean1369802.878
Median Absolute Deviation (MAD)0
Skewness-26.36285265
Sum952013000
Variance27005755.4
MonotonicityDecreasing
2021-11-30T22:34:08.442139image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
1370000694
99.9%
12330001
 
0.1%
ValueCountFrequency (%)
12330001
 
0.1%
1370000694
99.9%
ValueCountFrequency (%)
1370000694
99.9%
12330001
 
0.1%

relative_hour
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct695
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.510577938
Minimum0.5102777778
Maximum2.511111111
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2021-11-30T22:34:08.559013image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0.5102777778
5-th percentile0.6102777778
Q11.01
median1.510833333
Q32.010972222
95-th percentile2.411111111
Maximum2.511111111
Range2.000833333
Interquartile range (IQR)1.000972222

Descriptive statistics

Standard deviation0.5789679385
Coefficient of variation (CV)0.3832757808
Kurtosis-1.200471789
Mean1.510577938
Median Absolute Deviation (MAD)0.5016666667
Skewness-0.0002165559347
Sum1049.851667
Variance0.3352038738
MonotonicityStrictly increasing
2021-11-30T22:34:08.696516image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.51027777781
 
0.1%
1.8566666671
 
0.1%
1.8336111111
 
0.1%
1.8366666671
 
0.1%
1.8394444441
 
0.1%
1.8422222221
 
0.1%
1.8452777781
 
0.1%
1.8480555561
 
0.1%
1.8508333331
 
0.1%
1.8538888891
 
0.1%
Other values (685)685
98.6%
ValueCountFrequency (%)
0.51027777781
0.1%
0.51305555561
0.1%
0.51583333331
0.1%
0.51888888891
0.1%
0.52166666671
0.1%
ValueCountFrequency (%)
2.5111111111
0.1%
2.5083333331
0.1%
2.5052777781
0.1%
2.50251
0.1%
2.4997222221
0.1%

Interactions

2021-11-30T22:34:03.921275image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:48.281826image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:49.835936image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:51.209448image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:52.602871image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:54.095282image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:55.435873image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:56.975333image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:58.393131image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:59.808607image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:34:01.188239image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:34:02.547118image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:34:04.119273image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:48.398649image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:49.949649image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:51.324251image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:52.722886image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
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Correlations

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Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-11-30T22:34:08.999635image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-11-30T22:34:09.176874image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-11-30T22:34:09.370434image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

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A simple visualization of nullity by column.
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Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
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The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

df_indextscpu_loadcpu_freqmemory_usagecpu_tempgpu_memory_usagegpu_loadgpu_temphashrateunpaidreported_hashraterelative_hour
01772021-11-05 14:10:54-05:003.0971.41142544076829.03.052405e+09100.036.00.0NaN1370000.00.510278
11782021-11-05 14:11:04-05:000.3887.75142435942429.03.052405e+09100.040.00.0NaN1370000.00.513056
21792021-11-05 14:11:14-05:000.6866.82137860300829.03.052405e+09100.042.00.0NaN1370000.00.515833
31802021-11-05 14:11:25-05:000.21018.23137961062429.03.052405e+09100.043.00.0NaN1370000.00.518889
41812021-11-05 14:11:35-05:000.2911.78138008985629.03.052405e+09100.045.00.0NaN1370000.00.521667
51822021-11-05 14:11:46-05:000.1899.72137971302429.03.052405e+09100.046.00.0NaN1370000.00.524722
61832021-11-05 14:11:56-05:000.4954.24137946726430.03.052405e+09100.048.00.0NaN1370000.00.527500
71842021-11-05 14:12:06-05:000.3890.33138001203231.03.052405e+09100.049.00.0NaN1370000.00.530278
81852021-11-05 14:12:16-05:000.4869.65138008166430.03.052405e+09100.050.00.0NaN1370000.00.533056
91862021-11-05 14:12:27-05:000.4840.17137851289630.03.052405e+09100.051.00.0NaN1370000.00.536111

Last rows

df_indextscpu_loadcpu_freqmemory_usagecpu_tempgpu_memory_usagegpu_loadgpu_temphashrateunpaidreported_hashraterelative_hour
6858622021-11-05 16:09:24-05:000.7820.47137917644833.03.052405e+09100.065.03333333.332.363179e+151370000.02.485278
6868632021-11-05 16:09:34-05:000.7859.30137944268832.03.052405e+09100.065.03333333.332.363179e+151370000.02.488056
6878642021-11-05 16:09:44-05:000.51005.84137942630433.03.052405e+09100.065.03333333.332.363179e+151370000.02.490833
6888652021-11-05 16:09:55-05:000.3845.47137883648032.03.052405e+09100.065.03333333.332.363179e+151370000.02.493889
6898662021-11-05 16:10:05-05:000.3816.33137879961633.03.052405e+09100.065.03333333.332.363179e+151370000.02.496667
6908672021-11-05 16:10:16-05:000.3994.35137797632033.03.052405e+09100.065.03333333.332.363179e+151370000.02.499722
6918682021-11-05 16:10:26-05:000.6913.44137864806432.03.052405e+09100.065.03333333.332.363179e+151370000.02.502500
6928692021-11-05 16:10:36-05:000.2956.44137871360033.03.052405e+09100.065.03333333.332.363179e+151370000.02.505278
6938702021-11-05 16:10:47-05:000.4844.00137977036833.03.052405e+09100.065.03333333.332.363179e+151370000.02.508333
6948712021-11-05 16:10:57-05:000.3963.26123858944033.09.437184e+0690.064.03333333.332.363179e+151233000.02.511111